Introduction to Data Science

• Why to learn Data Science
• Scope of Data Science
• Jobs In Data Science

Introduction to Python & R

• Installation of Anaconda
• How to launch Jupyter Notebook, Spyder and R-Studio in Anaconda
• Shortcut keys of Jupyter Notebook and Spyder
• How to install Packages in python
• Download and Install R-Studio independently (without Anaconda)
• Shortcut keys of R-Studio
• How to install Packages in R-Studio
• Similarities between Python & R

Basics of Python & R

• Variables & Data Types
• Statements and loops
• Different types of operators
• Functions and Modules

Population & Sample

Types of Characteristics

• Attributes
• Variables

Types of Data

• Primary Data
• Secondary Data
• Cross-sectional Data
• Time Series Data
• Directional Data

Methods of Sampling

• Simple Random Sampling with and without Replacement
• Stratified Random Sampling
• Systematic Sampling
• Cluster Sampling
• Two Stage Sampling

Summary Statistics

Measure Central Tendency

• Arithmetic Mean
• Mode
• Median
• Mean
• Quartiles
• Deciles
• Percentiles
• Geometric Mean
• Harmonic Mean

Measure of Dispersion

• Range
• Semi interquartile Range (Quartile Deviation)
• Mean Deviation
• Variance
• Standard Deviation
• Mean Squared Deviation
• Coefficient of Variation(C.V.)

Moments, Skewness & Kurtosis

• Raw Moments
• Central moments
• Relation between Raw and Central Moments
• Concept of Skewness and Its Types
• Bowley’s Coefficient of Skewness
• Concept of kurtosis and Its Types

Correlation Analysis

• Types of Correlation Coefficients
• Karl Pearson’s Coefficient of Correlation
• Spearman’s Rank Correlation Coefficient
• Types of Correlation
• Positive Correlation (Poor, Moderate and Strong)
• Negative Correlation (Poor, Moderate and Strong)
• No Correlation

Probability

• Conditional Probability
• Multiplication theorem of Probability
• Bayes Theorem

Testing of Hypothesis

• Statistic (Estimator)
• Parameter
• Hypothesis
• Null Hypothesis
• Alternative Hypothesis
• Type I Error
• Type II Error
• Critical Region
• Test
• Test Statistic
• Level of Significance
• P-Value (Observed Level of Significance)
• Confidence Intervals

Test for Population Mean

• One Sample Z-Test
• Two sample Z-Test
• One Sample t-Test
• Two Sample t-Test

Central Limit Theorem

Test for Population Proportion

• One Sample population Proportion
• Two Sample Population Proportion

Paired t-Test

Test for Population Variance

• Chi-Square Test

Test for Goodness of Fit

Test for Independence of Two Attributes

Test for Equality of Population Variances

• F-Test

Analysis of Variance (ANOVA)

• Complete Randomised Design
• Randomised Block Design

Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Applications of Machine Learning

Data Visualisation with interpretation

• Bar plot
• Histogram
• Pie Chart
• Boxplot
• Scatter plot
• Many More……

Supervised Machine Learning

Regression Analysis

• Simple Linear Regression
• Multiple Linear Regression
• Non-linear Regression (Logarithmic, Exponential, Polynomial, Quadratic, and many more)
• Stepwise Regression (Forward Selection and backward elimination)
• Regularization Techniques (Ridge & Lasso Regression)

Classification

• Logistic Regression
• Naïve Bayes Algorithm
• K-Nearest Neighbours
• Support Vector Machine
• Decision Tree
• Ensemble Techniques
• Bagging (Random Forest)
• Boosting (Gradient Boosting, XGBoost, AdaBoost, etc.)
• Neural Networks (Multi-layer perceptron)

Unsupervised Machine Learning

Dimensionality Reduction Technique

• Principal Component Analysis

Clustering Analysis

• Hierarchical Clustering
• Single Linkage
• Complete Linkage
• Average Linkage
• Median Linkage
• Weighted Linkage
• Ward Linkage
• Non – Hierarchical Clustering
• K – Means

Association Rule & Recommendation System

• Measure of association
• Support
• Confidence
• Lift Ratio
• Market Basket Analysis (Affinity Analysis)
• Apriori Algorithm

Deep Learning

Introduction to Neural Network & Deep Learning

• Deep Learning Importance [Strength & Limitation]
• SP | MLP
• Neural Network Overview
• Neural Network Representation
• Activation Function
• Loss Function
• Importance of Non-linear Activation Function
• Gradient Descent for Neural Network

Parameter & Hyper parameter

• Train, Test & Validation Set
• Vanishing & Exploding Gradient
• Dropout
• Regularization
• Optimization algorithm
• Learning Rate
• Tuning
• Softmax

Convolutional Neural Network (CNN)

• Deep Convolution Model
• Detection Algorithm

Recurrent Neural Network (RNN)

• RNN
• LSTM
• Bi Directional LSTM